Added custom SD pipeline + euler_a scheduler

5 files changed, 869 insertions, 36 deletions

diff --git a/.gitignore b/.gitignore
index 4456cef..35b4c22 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,5 +160,5 @@ cython_debug/
 #.idea/
 
 output/
-conf*.json
+conf/
 v1-inference.yaml*
diff --git a/dreambooth.py b/dreambooth.py
index 39c4851..4d7366c 100644
--- a/dreambooth.py
+++ b/dreambooth.py
@@ -59,7 +59,7 @@ def parse_args():
     parser.add_argument(
         "--repeats",
         type=int,
-        default=100,
+        default=1,
         help="How many times to repeat the training data."
     )
     parser.add_argument(
@@ -375,7 +375,6 @@ class Checkpointer:
     @torch.no_grad()
     def save_samples(self, step, height, width, guidance_scale, eta, num_inference_steps):
         samples_path = Path(self.output_dir).joinpath("samples")
-        samples_path.mkdir(parents=True, exist_ok=True)
 
         unwrapped = self.accelerator.unwrap_model(self.unet)
         pipeline = StableDiffusionPipeline(
@@ -403,6 +402,7 @@ class Checkpointer:
 
             all_samples = []
             file_path = samples_path.joinpath("stable", f"step_{step}.png")
+            file_path.parent.mkdir(parents=True, exist_ok=True)
 
             data_enum = enumerate(val_data)
 
@@ -436,6 +436,7 @@ class Checkpointer:
         for data, pool in [(val_data, "val"), (train_data, "train")]:
             all_samples = []
             file_path = samples_path.joinpath(pool, f"step_{step}.png")
+            file_path.parent.mkdir(parents=True, exist_ok=True)
 
             data_enum = enumerate(data)
 
@@ -496,11 +497,15 @@ def main():
         cur_class_images = len(list(class_images_dir.iterdir()))
 
         if cur_class_images < args.num_class_images:
-            torch_dtype = torch.bfloat16 if accelerator.device.type == "cuda" else torch.float32
+            torch_dtype = torch.float32
+            if accelerator.device.type == "cuda":
+                torch_dtype = {"no": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}[args.mixed_precision]
+
             pipeline = StableDiffusionPipeline.from_pretrained(
                 args.pretrained_model_name_or_path, torch_dtype=torch_dtype)
             pipeline.enable_attention_slicing()
             pipeline.set_progress_bar_config(disable=True)
+            pipeline.to(accelerator.device)
 
             num_new_images = args.num_class_images - cur_class_images
             logger.info(f"Number of class images to sample: {num_new_images}.")
@@ -509,7 +514,6 @@ def main():
             sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
 
             sample_dataloader = accelerator.prepare(sample_dataloader)
-            pipeline.to(accelerator.device)
 
             for example in tqdm(
                 sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
diff --git a/infer.py b/infer.py
index f2007e9..de3d792 100644
--- a/infer.py
+++ b/infer.py
@@ -1,18 +1,15 @@
 import argparse
 import datetime
+import logging
 from pathlib import Path
 from torch import autocast
-from diffusers import StableDiffusionPipeline
 import torch
 import json
-from diffusers import AutoencoderKL, StableDiffusionPipeline, UNet2DConditionModel, PNDMScheduler
-from transformers import CLIPTextModel, CLIPTokenizer, CLIPFeatureExtractor
+from diffusers import StableDiffusionPipeline, AutoencoderKL, UNet2DConditionModel, PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler
+from transformers import CLIPModel, CLIPTextModel, CLIPTokenizer, CLIPFeatureExtractor
 from slugify import slugify
-from pipelines.stable_diffusion.no_check import NoCheck
-
-model_id = "path-to-your-trained-model"
-
-prompt = "A photo of sks dog in a bucket"
+from pipelines.stable_diffusion.clip_guided_stable_diffusion import CLIPGuidedStableDiffusion
+from schedulers.scheduling_euler_a import EulerAScheduler
 
 
 def parse_args():
@@ -30,6 +27,21 @@ def parse_args():
         default=None,
     )
     parser.add_argument(
+        "--negative_prompt",
+        type=str,
+        default=None,
+    )
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=512,
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=512,
+    )
+    parser.add_argument(
         "--batch_size",
         type=int,
         default=1,
@@ -42,17 +54,28 @@ def parse_args():
     parser.add_argument(
         "--steps",
         type=int,
-        default=80,
+        default=120,
     )
     parser.add_argument(
-        "--scale",
+        "--scheduler",
+        type=str,
+        choices=["plms", "ddim", "klms", "euler_a"],
+        default="euler_a",
+    )
+    parser.add_argument(
+        "--guidance_scale",
         type=int,
         default=7.5,
     )
     parser.add_argument(
+        "--clip_guidance_scale",
+        type=int,
+        default=100,
+    )
+    parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=torch.random.seed(),
     )
     parser.add_argument(
         "--output_dir",
@@ -81,31 +104,39 @@ def save_args(basepath, args, extra={}):
         json.dump(info, f, indent=4)
 
 
-def main():
-    args = parse_args()
-
-    seed = args.seed or torch.random.seed()
-
-    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-    output_dir = Path(args.output_dir).joinpath(f"{now}_{slugify(args.prompt)[:100]}")
-    output_dir.mkdir(parents=True, exist_ok=True)
-    save_args(output_dir, args)
-
+def gen(args, output_dir):
     tokenizer = CLIPTokenizer.from_pretrained(args.model + '/tokenizer', torch_dtype=torch.bfloat16)
     text_encoder = CLIPTextModel.from_pretrained(args.model + '/text_encoder', torch_dtype=torch.bfloat16)
+    clip_model = CLIPModel.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.bfloat16)
     vae = AutoencoderKL.from_pretrained(args.model + '/vae', torch_dtype=torch.bfloat16)
     unet = UNet2DConditionModel.from_pretrained(args.model + '/unet', torch_dtype=torch.bfloat16)
-    feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32", torch_dtype=torch.bfloat16)
+    feature_extractor = CLIPFeatureExtractor.from_pretrained(
+        "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.bfloat16)
 
-    pipeline = StableDiffusionPipeline(
+    if args.scheduler == "plms":
+        scheduler = PNDMScheduler(
+            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
+        )
+    elif args.scheduler == "klms":
+        scheduler = LMSDiscreteScheduler(
+            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
+        )
+    elif args.scheduler == "ddim":
+        scheduler = DDIMScheduler(
+            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False
+        )
+    else:
+        scheduler = EulerAScheduler(
+            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False
+        )
+
+    pipeline = CLIPGuidedStableDiffusion(
         text_encoder=text_encoder,
         vae=vae,
         unet=unet,
         tokenizer=tokenizer,
-        scheduler=PNDMScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
-        ),
-        safety_checker=NoCheck(),
+        clip_model=clip_model,
+        scheduler=scheduler,
         feature_extractor=feature_extractor
     )
     pipeline.enable_attention_slicing()
@@ -113,16 +144,34 @@ def main():
 
     with autocast("cuda"):
         for i in range(args.batch_num):
-            generator = torch.Generator(device="cuda").manual_seed(seed + i)
+            generator = torch.Generator(device="cuda").manual_seed(args.seed + i)
             images = pipeline(
-                [args.prompt] * args.batch_size,
+                prompt=[args.prompt] * args.batch_size,
+                height=args.height,
+                width=args.width,
+                negative_prompt=args.negative_prompt,
                 num_inference_steps=args.steps,
-                guidance_scale=args.scale,
+                guidance_scale=args.guidance_scale,
+                clip_guidance_scale=args.clip_guidance_scale,
                 generator=generator,
             ).images
 
             for j, image in enumerate(images):
-                image.save(output_dir.joinpath(f"{seed + i}_{j}.jpg"))
+                image.save(output_dir.joinpath(f"{args.seed + i}_{j}.jpg"))
+
+
+def main():
+    args = parse_args()
+
+    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+    output_dir = Path(args.output_dir).joinpath(f"{now}_{slugify(args.prompt)[:100]}")
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    save_args(output_dir, args)
+
+    logging.basicConfig(filename=output_dir.joinpath("log.txt"), level=logging.DEBUG)
+
+    gen(args, output_dir)
 
 
 if __name__ == "__main__":
diff --git a/pipelines/stable_diffusion/clip_guided_stable_diffusion.py b/pipelines/stable_diffusion/clip_guided_stable_diffusion.py
new file mode 100644
index 0000000..306d9a9
--- /dev/null
+++ b/pipelines/stable_diffusion/clip_guided_stable_diffusion.py
@@ -0,0 +1,457 @@
+import inspect
+import warnings
+from typing import List, Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from diffusers.configuration_utils import FrozenDict
+from diffusers import AutoencoderKL, DiffusionPipeline, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import logging
+from torchvision import transforms
+from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
+from schedulers.scheduling_euler_a import EulerAScheduler, CFGDenoiserForward
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class MakeCutouts(nn.Module):
+    def __init__(self, cut_size, cut_power=1.0):
+        super().__init__()
+
+        self.cut_size = cut_size
+        self.cut_power = cut_power
+
+    def forward(self, pixel_values, num_cutouts):
+        sideY, sideX = pixel_values.shape[2:4]
+        max_size = min(sideX, sideY)
+        min_size = min(sideX, sideY, self.cut_size)
+        cutouts = []
+        for _ in range(num_cutouts):
+            size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
+            offsetx = torch.randint(0, sideX - size + 1, ())
+            offsety = torch.randint(0, sideY - size + 1, ())
+            cutout = pixel_values[:, :, offsety: offsety + size, offsetx: offsetx + size]
+            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
+        return torch.cat(cutouts)
+
+
+def spherical_dist_loss(x, y):
+    x = F.normalize(x, dim=-1)
+    y = F.normalize(y, dim=-1)
+    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
+
+
+def set_requires_grad(model, value):
+    for param in model.parameters():
+        param.requires_grad = value
+
+
+class CLIPGuidedStableDiffusion(DiffusionPipeline):
+    """CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
+    - https://github.com/Jack000/glid-3-xl
+    - https://github.dev/crowsonkb/k-diffusion
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        clip_model: CLIPModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        feature_extractor: CLIPFeatureExtractor,
+        **kwargs,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            warnings.warn(
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file",
+                DeprecationWarning,
+            )
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            clip_model=clip_model,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+        )
+
+        self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
+        self.make_cutouts = MakeCutouts(feature_extractor.size)
+
+        set_requires_grad(self.text_encoder, False)
+        set_requires_grad(self.clip_model, False)
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    def freeze_vae(self):
+        set_requires_grad(self.vae, False)
+
+    def unfreeze_vae(self):
+        set_requires_grad(self.vae, True)
+
+    def freeze_unet(self):
+        set_requires_grad(self.unet, False)
+
+    def unfreeze_unet(self):
+        set_requires_grad(self.unet, True)
+
+    @torch.enable_grad()
+    def cond_fn(
+        self,
+        latents,
+        timestep,
+        index,
+        text_embeddings,
+        noise_pred_original,
+        text_embeddings_clip,
+        clip_guidance_scale,
+        num_cutouts,
+        use_cutouts=True,
+    ):
+        latents = latents.detach().requires_grad_()
+
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            sigma = self.scheduler.sigmas[index]
+            # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
+            latent_model_input = latents / ((sigma**2 + 1) ** 0.5)
+        else:
+            latent_model_input = latents
+
+        # predict the noise residual
+        noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
+
+        if isinstance(self.scheduler, PNDMScheduler):
+            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
+            beta_prod_t = 1 - alpha_prod_t
+            # compute predicted original sample from predicted noise also called
+            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
+
+            fac = torch.sqrt(beta_prod_t)
+            sample = pred_original_sample * (fac) + latents * (1 - fac)
+        elif isinstance(self.scheduler, LMSDiscreteScheduler):
+            sigma = self.scheduler.sigmas[index]
+            sample = latents - sigma * noise_pred
+        else:
+            raise ValueError(f"scheduler type {type(self.scheduler)} not supported")
+
+        sample = 1 / 0.18215 * sample
+        image = self.vae.decode(sample).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        if use_cutouts:
+            image = self.make_cutouts(image, num_cutouts)
+        else:
+            image = transforms.Resize(self.feature_extractor.size)(image)
+        image = self.normalize(image)
+
+        image_embeddings_clip = self.clip_model.get_image_features(image).float()
+        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+
+        if use_cutouts:
+            dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
+            dists = dists.view([num_cutouts, sample.shape[0], -1])
+            loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
+        else:
+            loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale
+
+        grads = -torch.autograd.grad(loss, latents)[0]
+
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents.detach() + grads * (sigma**2)
+            noise_pred = noise_pred_original
+        else:
+            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
+        return noise_pred, latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        eta: Optional[float] = 0.0,
+        clip_guidance_scale: Optional[float] = 100,
+        clip_prompt: Optional[Union[str, List[str]]] = None,
+        num_cutouts: Optional[int] = 4,
+        use_cutouts: Optional[bool] = True,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is None:
+            negative_prompt = [""] * batch_size
+        elif isinstance(negative_prompt, str):
+            negative_prompt = [negative_prompt] * batch_size
+        elif isinstance(negative_prompt, list):
+            if len(negative_prompt) != batch_size:
+                raise ValueError(
+                    f"`prompt` and `negative_prompt` have to be the same length, but are {len(prompt)} and {len(negative_prompt)}")
+        else:
+            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length:])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        if clip_guidance_scale > 0:
+            if clip_prompt is not None:
+                clip_text_inputs = self.tokenizer(
+                    clip_prompt,
+                    padding="max_length",
+                    max_length=self.tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+                clip_text_input_ids = clip_text_inputs.input_ids
+            else:
+                clip_text_input_ids = text_input_ids
+            text_embeddings_clip = self.clip_model.get_text_features(clip_text_input_ids.to(self.device))
+            text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                negative_prompt, padding="max_length", max_length=max_length, return_tensors="pt"
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_device = "cpu" if self.device.type == "mps" else self.device
+        latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
+        if latents is None:
+            latents = torch.randn(
+                latents_shape,
+                generator=generator,
+                device=latents_device,
+                dtype=text_embeddings.dtype,
+            )
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+        latents = latents.to(self.device)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimzed to move all timesteps to correct device beforehand
+        if torch.is_tensor(self.scheduler.timesteps):
+            timesteps_tensor = self.scheduler.timesteps.to(self.device)
+        else:
+            timesteps_tensor = torch.tensor(self.scheduler.timesteps.copy(), device=self.device)
+
+        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents * self.scheduler.sigmas[0]
+        elif isinstance(self.scheduler, EulerAScheduler):
+            sigma = self.scheduler.timesteps[0]
+            latents = latents * sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        scheduler_step_args = set(inspect.signature(self.scheduler.step).parameters.keys())
+        accepts_eta = "eta" in scheduler_step_args
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+        accepts_generator = "generator" in scheduler_step_args
+        if generator is not None and accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            if isinstance(self.scheduler, LMSDiscreteScheduler):
+                sigma = self.scheduler.sigmas[i]
+                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
+                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
+
+            noise_pred = None
+            if isinstance(self.scheduler, EulerAScheduler):
+                sigma = t.reshape(1)
+                sigma_in = torch.cat([sigma] * 2)
+                # noise_pred = model(latent_model_input,sigma_in,uncond_embeddings, text_embeddings,guidance_scale)
+                noise_pred = CFGDenoiserForward(self.unet, latent_model_input, sigma_in,
+                                                text_embeddings, guidance_scale, DSsigmas=self.scheduler.DSsigmas)
+                # noise_pred = self.unet(latent_model_input, sigma_in, encoder_hidden_states=text_embeddings).sample
+            else:
+                # predict the noise residual
+                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # perform clip guidance
+            if clip_guidance_scale > 0:
+                text_embeddings_for_guidance = (
+                    text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
+                )
+                noise_pred, latents = self.cond_fn(
+                    latents,
+                    t,
+                    i,
+                    text_embeddings_for_guidance,
+                    noise_pred,
+                    text_embeddings_clip,
+                    clip_guidance_scale,
+                    num_cutouts,
+                    use_cutouts,
+                )
+
+            # compute the previous noisy sample x_t -> x_t-1
+            if isinstance(self.scheduler, LMSDiscreteScheduler):
+                latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
+            elif isinstance(self.scheduler, EulerAScheduler):
+                if i < self.scheduler.timesteps.shape[0] - 1:  # avoid out of bound error
+                    t_prev = self.scheduler.timesteps[i+1]
+                    latents = self.scheduler.step(noise_pred, t, t_prev, latents, **extra_step_kwargs).prev_sample
+            else:
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+        # scale and decode the image latents with vae
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, None)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
diff --git a/schedulers/scheduling_euler_a.py b/schedulers/scheduling_euler_a.py
new file mode 100644
index 0000000..57a56de
--- /dev/null
+++ b/schedulers/scheduling_euler_a.py
@@ -0,0 +1,323 @@
+
+
+import math
+import warnings
+from typing import Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
+
+
+'''
+helper functions:   append_zero(),
+                    t_to_sigma(),
+                    get_sigmas(),
+                    append_dims(),
+                    CFGDenoiserForward(),
+                    get_scalings(),
+                    DSsigma_to_t(),
+                    DiscreteEpsDDPMDenoiserForward(),
+                    to_d(),
+                    get_ancestral_step()
+need cleaning 
+'''
+
+
+def append_zero(x):
+    return torch.cat([x, x.new_zeros([1])])
+
+
+def t_to_sigma(t, sigmas):
+    t = t.float()
+    low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
+    return (1 - w) * sigmas[low_idx] + w * sigmas[high_idx]
+
+
+def get_sigmas(sigmas, n=None):
+    if n is None:
+        return append_zero(sigmas.flip(0))
+    t_max = len(sigmas) - 1  # = 999
+    t = torch.linspace(t_max, 0, n, device=sigmas.device)
+    # t = torch.linspace(t_max, 0, n, device=sigmas.device)
+    return append_zero(t_to_sigma(t, sigmas))
+
+# from k_samplers utils.py
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def CFGDenoiserForward(Unet, x_in, sigma_in, cond_in, cond_scale, DSsigmas=None):
+    # x_in = torch.cat([x] * 2)#A# concat the latent
+    # sigma_in = torch.cat([sigma] * 2) #A# concat sigma
+    # cond_in = torch.cat([uncond, cond])
+    # uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+    # uncond, cond = DiscreteEpsDDPMDenoiserForward(Unet,x_in, sigma_in,DSsigmas=DSsigmas, cond=cond_in).chunk(2)
+    # return uncond + (cond - uncond) * cond_scale
+    noise_pred = DiscreteEpsDDPMDenoiserForward(Unet, x_in, sigma_in, DSsigmas=DSsigmas, cond=cond_in)
+    return noise_pred
+
+# from k_samplers sampling.py
+
+
+def to_d(x, sigma, denoised):
+    """Converts a denoiser output to a Karras ODE derivative."""
+    return (x - denoised) / append_dims(sigma.to(denoised.device), x.ndim)
+
+
+def get_scalings(sigma):
+    sigma_data = 1.
+    c_out = -sigma
+    c_in = 1 / (sigma ** 2 + sigma_data ** 2) ** 0.5
+    return c_out, c_in
+
+# DiscreteSchedule DS
+
+
+def DSsigma_to_t(sigma, quantize=None, DSsigmas=None):
+    # quantize = self.quantize if quantize is None else quantize
+    quantize = False
+    dists = torch.abs(sigma - DSsigmas[:, None])
+    if quantize:
+        return torch.argmin(dists, dim=0).view(sigma.shape)
+    low_idx, high_idx = torch.sort(torch.topk(dists, dim=0, k=2, largest=False).indices, dim=0)[0]
+    low, high = DSsigmas[low_idx], DSsigmas[high_idx]
+    w = (low - sigma) / (low - high)
+    w = w.clamp(0, 1)
+    t = (1 - w) * low_idx + w * high_idx
+    return t.view(sigma.shape)
+
+
+def DiscreteEpsDDPMDenoiserForward(Unet, input, sigma, DSsigmas=None, **kwargs):
+    sigma = sigma.to(Unet.device)
+    DSsigmas = DSsigmas.to(Unet.device)
+    c_out, c_in = [append_dims(x, input.ndim) for x in get_scalings(sigma)]
+    # ??? what is eps?
+    # eps = CVDget_eps(Unet,input * c_in, DSsigma_to_t(sigma), **kwargs)
+    eps = Unet(input * c_in, DSsigma_to_t(sigma, DSsigmas=DSsigmas),
+               encoder_hidden_states=kwargs['cond']).sample
+    return input + eps * c_out
+
+
+# from k_samplers sampling.py
+def get_ancestral_step(sigma_from, sigma_to):
+    """Calculates the noise level (sigma_down) to step down to and the amount
+    of noise to add (sigma_up) when doing an ancestral sampling step."""
+    sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5
+    sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
+    return sigma_down, sigma_up
+
+
+'''
+Euler Ancestral Scheduler
+'''
+
+
+class EulerAScheduler(SchedulerMixin, ConfigMixin):
+    """
+    Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
+    the VE column of Table 1 from [1] for reference.
+
+    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
+    https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
+    differential equations." https://arxiv.org/abs/2011.13456
+
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functions.
+
+    For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of
+    Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the
+    optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper.
+
+    Args:
+        sigma_min (`float`): minimum noise magnitude
+        sigma_max (`float`): maximum noise magnitude
+        s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling.
+            A reasonable range is [1.000, 1.011].
+        s_churn (`float`): the parameter controlling the overall amount of stochasticity.
+            A reasonable range is [0, 100].
+        s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity).
+            A reasonable range is [0, 10].
+        s_max (`float`): the end value of the sigma range where we add noise.
+            A reasonable range is [0.2, 80].
+
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.0001,
+        beta_end: float = 0.02,
+        beta_schedule: str = "linear",
+        trained_betas: Optional[np.ndarray] = None,
+        clip_sample: bool = True,
+        set_alpha_to_one: bool = True,
+        steps_offset: int = 0,
+    ):
+        if trained_betas is not None:
+            self.betas = torch.from_numpy(trained_betas)
+        if beta_schedule == "linear":
+            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+        elif beta_schedule == "scaled_linear":
+            # this schedule is very specific to the latent diffusion model.
+            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
+        else:
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+
+        # At every step in ddim, we are looking into the previous alphas_cumprod
+        # For the final step, there is no previous alphas_cumprod because we are already at 0
+        # `set_alpha_to_one` decides whether we set this parameter simply to one or
+        # whether we use the final alpha of the "non-previous" one.
+        self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
+
+        # setable values
+        self.num_inference_steps = None
+        self.timesteps = np.arange(0, num_train_timesteps)[::-1]
+
+    # A# take number of steps as input
+    # A# store 1) number of steps 2) timesteps 3) schedule
+
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, **kwargs):
+        """
+        Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
+
+        Args:
+            num_inference_steps (`int`):
+                the number of diffusion steps used when generating samples with a pre-trained model.
+        """
+
+        # offset = self.config.steps_offset
+
+        # if "offset" in kwargs:
+        #     warnings.warn(
+        #         "`offset` is deprecated as an input argument to `set_timesteps` and will be removed in v0.4.0."
+        #         " Please pass `steps_offset` to `__init__` instead.",
+        #         DeprecationWarning,
+        #     )
+
+        #     offset = kwargs["offset"]
+
+        self.num_inference_steps = num_inference_steps
+        self.DSsigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
+        self.sigmas = get_sigmas(self.DSsigmas, self.num_inference_steps).to(device=device)
+        self.timesteps = self.sigmas
+
+    def add_noise_to_input(
+        self, sample: torch.FloatTensor, sigma: float, generator: Optional[torch.Generator] = None
+    ) -> Tuple[torch.FloatTensor, float]:
+        """
+        Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a
+        higher noise level sigma_hat = sigma_i + gamma_i*sigma_i.
+
+        TODO Args:
+        """
+        if self.config.s_min <= sigma <= self.config.s_max:
+            gamma = min(self.config.s_churn / self.num_inference_steps, 2**0.5 - 1)
+        else:
+            gamma = 0
+
+        # sample eps ~ N(0, S_noise^2 * I)
+        eps = self.config.s_noise * torch.randn(sample.shape, generator=generator).to(sample.device)
+        sigma_hat = sigma + gamma * sigma
+        sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
+
+        return sample_hat, sigma_hat
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: torch.IntTensor,
+        timestep_prev: torch.IntTensor,
+        sample: torch.FloatTensor,
+        generator: None,
+        # ,sigma_hat: float,
+        #  sigma_prev: float,
+        # sample_hat: torch.FloatTensor,
+        return_dict: bool = True,
+    ) -> Union[SchedulerOutput, Tuple]:
+        """
+        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+            sigma_hat (`float`): TODO
+            sigma_prev (`float`): TODO
+            sample_hat (`torch.FloatTensor`): TODO
+            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+
+            EulerAOutput: updated sample in the diffusion chain and derivative (TODO double check).
+        Returns:
+            [`~schedulers.scheduling_karras_ve.EulerAOutput`] or `tuple`:
+            [`~schedulers.scheduling_karras_ve.EulerAOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
+
+        """
+        latents = sample
+        sigma_down, sigma_up = get_ancestral_step(timestep, timestep_prev)
+
+        # if callback is not None:
+        #     callback({'x': latents, 'i': i, 'sigma': timestep, 'sigma_hat': timestep, 'denoised': model_output})
+        d = to_d(latents, timestep, model_output)
+        # Euler method
+        dt = sigma_down - timestep
+        latents = latents + d * dt
+        latents = latents + torch.randn(latents.shape, layout=latents.layout, device=latents.device,
+                                        generator=generator) * sigma_up
+        return SchedulerOutput(prev_sample=latents)
+
+    def step_correct(
+        self,
+        model_output: torch.FloatTensor,
+        sigma_hat: float,
+        sigma_prev: float,
+        sample_hat: torch.FloatTensor,
+        sample_prev: torch.FloatTensor,
+        derivative: torch.FloatTensor,
+        generator: None,
+        return_dict: bool = True,
+    ) -> Union[SchedulerOutput, Tuple]:
+        """
+        Correct the predicted sample based on the output model_output of the network. TODO complete description
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+            sigma_hat (`float`): TODO
+            sigma_prev (`float`): TODO
+            sample_hat (`torch.FloatTensor`): TODO
+            sample_prev (`torch.FloatTensor`): TODO
+            derivative (`torch.FloatTensor`): TODO
+            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+
+        Returns:
+            prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO
+
+        """
+        pred_original_sample = sample_prev + sigma_prev * model_output
+        derivative_corr = (sample_prev - pred_original_sample) / sigma_prev
+        sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
+
+        if not return_dict:
+            return (sample_prev, derivative)
+
+        return SchedulerOutput(prev_sample=sample_prev)
+
+    def add_noise(self, original_samples, noise, timesteps):
+        raise NotImplementedError()